No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation

TL;DR

This paper introduces a dual deep interaction channel network leveraging semantic, emotional, and data augmentation techniques to improve fake news detection, addressing challenges of feature mining and small sample sizes.

Contribution

It proposes a novel framework combining semantic, emotional, and data enhancement strategies with a dual interaction network for more effective fake news detection.

Findings

Outperforms state-of-the-art methods in accuracy

Effectively mines emotional evolution patterns

Enhances model performance with data augmentation

Abstract

Online Social Network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments. Meanwhile, the framework introduces a data enhancement module to obtain more labeled data with high quality based on confidence which further improves the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods.